{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test bilinear sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import interpolate\n",
    "x = np.arange(-5.01, 5.01, 0.25)\n",
    "y = np.arange(-5.01, 5.01, 0.25)\n",
    "xx, yy = np.meshgrid(x, y)\n",
    "z = np.sin(xx**2+yy**2)\n",
    "f = interpolate.interp2d(x, y, z, kind='cubic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xnew = np.arange(-5.01, 5.01, 1e-2)\n",
    "ynew = np.arange(-5.01, 5.01, 1e-2)\n",
    "znew = f(xnew, ynew)\n",
    "plt.plot(x, z[0, :], 'ro-', xnew, znew[0, :], 'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test gaussian kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/yoyee/Documents/deepSfm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "# add your module path\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "# change your base path\n",
    "os.chdir('../')\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a:  [[0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAACKJJREFUeJzt3U+InPUdx/HPp5tIpLZ4qIeQDY0HEUKgEUoQ7KEIgbSG2qOCnoS9VEigRWwPBU+9FPHSS7BBoUUR9CChEAIN2IKN+WNsTaIlSIsRYSmhaBAqMd8eZgKxded5NvM8+8zzyfsFCzOTZ579Eua9z/PMLL91VQlApq8NPQCA/hA4EIzAgWAEDgQjcCAYgQPBCBwIRuBAMAIHgm3qY6e2+fU4oGdV5aZtOIIDwQgcCEbgQDACB4IROBCMwIFgBA4EI3AgGIEDwQgcCEbgQDACB4IROBCMwIFgBA4EI3AgGIEDwVoFbnuf7fdtX7T9dN9DAeiGm/74oO0lSX+XtFfSJUknJT1aVednPIclm4CedbVk0x5JF6vqg6r6XNLLkh6edzgA/WsT+DZJH95w/9L0MQALrrNVVW2vSFrpan8A5tcm8I8kbb/h/vL0sS+pqkOSDklcgwOLos0p+klJ99i+2/Ztkh6R9Hq/YwHoQuMRvKqu2n5S0lFJS5IOV9W53icDMLfGj8luaqecogO94y+bALc4AgeCETgQjMCBYAQOBCNwIBiBA8EIHAhG4EAwAgeCETgQjMCBYAQOBCNwIBiBA8EIHAhG4EAwAgeCETgQjMCBYAQOBCNwIBiBA8EIHAhG4EAwAgeCETgQjMCBYAQOBCNwIBiBA8EIHAhG4EAwAgeCETgQrDFw24dtr9p+dyMGAtCdNkfwFyTt63kOAD1oDLyq3pB0eQNmAdAxrsGBYJu62pHtFUkrXe0PwPxcVc0b2TskHamqXa12ajfvFMBcqspN23CKDgRr8zHZS5LelHSv7Uu2n+h/LABdaHWKvu6dcooO9I5TdOAWR+BAMAIHghE4EIzAgWAEDgQjcCAYgQPBCBwIRuBAMAIHghE4EIzAgWAEDgQjcCAYgQPBOlt0ERujjwU6+mQ3rkmAHnEEB4IROBCMwIFgBA4EI3AgGIEDwQgcCEbgQDACB4IROBCMwIFgBA4EI3AgGIEDwQgcCEbgQDACB4I1Bm57u+3jts/bPmf7wEYMBmB+bloCyPZWSVur6oztb0g6LenHVXV+xnPGta7QiLBkE66rqsb/3MYjeFV9XFVnprc/lXRB0rb5xwPQt3Vdg9veIek+SSf6GAZAt1qvqmr7DkmvSjpYVZ98xb+vSFrpcDYAc2q8Bpck25slHZF0tKqebbH9uC4UR4RrcFzX5hq8zZtslvSipMtVdbDNNybw/hA4rusq8O9J+pOkv0m6Nn34F1X1hxnPGdercEQIHNd1EvjNIPD+EDiu6+RjMgDjReBAMAIHghE4EIzAgWAEDgQjcCAYgQPBCBwIRuBAMAIHghE4EIzAgWAEDgQjcCAYgQPBCBwI1npVVSwGVkjBenAEB4IROBCMwIFgBA4EI3AgGIEDwQgcCEbgQDACB4IROBCMwIFgBA4EI3AgGIEDwQgcCEbgQDACB4I1Bm57i+23bL9j+5ztZzZiMADzc1XN3mCyRtDXq+qK7c2S/izpQFX9ZcZzZu8UwNyqqnH9rsY12WryE+DK9O7m6RcBAyPQ6hrc9pLts5JWJR2rqhP9jgWgC60Cr6ovqmq3pGVJe2zv+t9tbK/YPmX7VNdDArg5jdfg//cE+5eSPquqX8/YhlN4oGdtrsHbvIt+l+07p7dvl7RX0nvzjwegb23+8MFWSS/aXtLkB8IrVXWk37EAdGHdp+itdsopOtC7Tk7RAYwXgQPBCBwIRuBAMAIHghE4EIzAgWAEDgQjcCAYgQPBCBwIRuBAMAIHghE4EIzAgWAEDgQjcCAYgQPBCBwIRuBAMAIHghE4EIzAgWAEDgQjcCAYgQPBCBwIRuBAMAIHghE4EIzAgWAEDgQjcCAYgQPBWgdue8n227aP9DkQgO6s5wh+QNKFvgYB0L1WgdtelvSQpOf7HQdAl9oewZ+T9JSkaz3OAqBjjYHb3i9ptapON2y3YvuU7VOdTQdgLq6q2RvYv5L0uKSrkrZI+qak16rqsRnPmb1TAHOrKjdt0xj4lza2vy/pZ1W1v2E7Agd61iZwPgcHgq3rCN56pxzBgd5xBAducQQOBCNwIBiBA8EIHAhG4EAwAgeCETgQjMCBYAQOBCNwIBiBA8EIHAhG4EAwAgeCETgQjMCBYJt62u+/JP2z431+a7rfsRjTvGOaVRrXvH3N+u02G/WyZFMfbJ+qqu8OPUdbY5p3TLNK45p36Fk5RQeCETgQbEyBHxp6gHUa07xjmlUa17yDzjqaa3AA6zemIziAdRpF4Lb32X7f9kXbTw89zyy2D9tetf3u0LM0sb3d9nHb522fs31g6JnWYnuL7bdsvzOd9ZmhZ2rD9pLtt20fGeL7L3zgtpck/UbSDyTtlPSo7Z3DTjXTC5L2DT1ES1cl/bSqdkq6X9JPFvj/9j+SHqyq70jaLWmf7fsHnqmNA5IuDPXNFz5wSXskXayqD6rqc0kvS3p44JnWVFVvSLo89BxtVNXHVXVmevtTTV6I24ad6qvVxJXp3c3Tr4V+A8n2sqSHJD0/1AxjCHybpA9vuH9JC/oiHDPbOyTdJ+nEsJOsbXq6e1bSqqRjVbWws049J+kpSdeGGmAMgaNntu+Q9Kqkg1X1ydDzrKWqvqiq3ZKWJe2xvWvomdZie7+k1ao6PeQcYwj8I0nbb7i/PH0MHbC9WZO4f19Vrw09TxtV9W9Jx7XY73U8IOlHtv+hyWXlg7Z/t9FDjCHwk5LusX237dskPSLp9YFnimDbkn4r6UJVPTv0PLPYvsv2ndPbt0vaK+m9YadaW1X9vKqWq2qHJq/ZP1bVYxs9x8IHXlVXJT0p6agmbwK9UlXnhp1qbbZfkvSmpHttX7L9xNAzzfCApMc1ObqcnX79cOih1rBV0nHbf9Xkh/6xqhrko6cx4TfZgGALfwQHcPMIHAhG4EAwAgeCETgQjMCBYAQOBCNwINh/Abjy8g8EhovqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_filtered:  [[0.03989173 0.0399667  0.04001229 0.0399667  0.03989173]\n",
      " [0.0399667  0.0400418  0.04008748 0.0400418  0.0399667 ]\n",
      " [0.04001229 0.04008748 0.0401332  0.04008748 0.04001229]\n",
      " [0.0399667  0.0400418  0.04008748 0.0400418  0.0399667 ]\n",
      " [0.03989173 0.0399667  0.04001229 0.0399667  0.03989173]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.ndimage import gaussian_filter\n",
    "a = np.arange(50, step=2).reshape((5,5))\n",
    "a = np.zeros((5,5))\n",
    "a[2,2] = 1\n",
    "print(\"a: \", a)\n",
    "plt.imshow(a)\n",
    "plt.show()\n",
    "\n",
    "a_filtered = gaussian_filter(a, sigma=3)\n",
    "print(\"a_filtered: \", a_filtered)\n",
    "plt.imshow(a_filtered)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import misc\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "plt.gray()  # show the filtered result in grayscale\n",
    "ax1 = fig.add_subplot(121)  # left side\n",
    "ax2 = fig.add_subplot(122)  # right side\n",
    "ascent = misc.ascent()\n",
    "result = gaussian_filter(ascent, sigma=5)\n",
    "ax1.imshow(ascent)\n",
    "ax2.imshow(result)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## extropolate points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "def extrapolate_points(pnts):\n",
    "    pnts_int = pnts.long().type(torch.FloatTensor)\n",
    "    pnts_x, pnts_y = pnts_int[:,0], pnts_int[:,1]\n",
    "\n",
    "    stack_1 = lambda x, y: torch.stack((x, y), dim=1)\n",
    "    pnts_ext = torch.cat((pnts_int, stack_1(pnts_x, pnts_y+1),\n",
    "        stack_1(pnts_x+1, pnts_y), pnts_int+1), dim=0)\n",
    "\n",
    "    pnts_res = pnts - pnts_int # (x, y)\n",
    "    x_res, y_res = pnts_res[:,0], pnts_res[:,1] # residuals\n",
    "    res_ext = torch.cat(((1-x_res)*(1-y_res), (1-x_res)*y_res, \n",
    "            x_res*(1-y_res), x_res*y_res), dim=0)\n",
    "    return pnts_ext, res_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pnts_ext:  tensor([[2., 2.],\n",
      "        [2., 3.],\n",
      "        [3., 2.],\n",
      "        [3., 3.]])\n",
      "res_ext:  tensor([0.4800, 0.3200, 0.1200, 0.0800])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "# pnts = torch.tensor([[1.5, 1.5],[2.2, 2.4]])\n",
    "pnts = torch.tensor([[2.2, 2.4]])\n",
    "pnts_ext, res_ext = extrapolate_points(pnts)\n",
    "print(\"pnts_ext: \", pnts_ext)\n",
    "print(\"res_ext: \", res_ext)\n",
    "# torch.cat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gaussian augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images: (2, 5, 5, 1)\n",
      "images_aug[0].squeeze()  [[  0   0   0   0   0]\n",
      " [  0   0   0   0   0]\n",
      " [  0   0 255   0   0]\n",
      " [  0   0   0   0   0]\n",
      " [  0   0   0   0   0]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import imgaug.augmenters as iaa\n",
    "from imgaug import parameters as iap\n",
    "\n",
    "images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)\n",
    "images = np.zeros((2,5,5,1), dtype=np.uint8)\n",
    "images[:,2,2] = 255\n",
    "seq = iaa.Sequential([iaa.GaussianBlur((3.0))])\n",
    "\n",
    "# images_aug = seq(images)\n",
    "# images_aug = iaa.GaussianBlur(3.0)(images=images)\n",
    "\n",
    "# Show an image with 8*8 augmented versions of image 0 and 8*8 augmented\n",
    "# versions of image 1. Identical augmentations will be applied to\n",
    "# image 0 and 1.\n",
    "# seq.show_grid([images[0], images[1]], cols=8, rows=8)\n",
    "\n",
    "blurer = iaa.GaussianBlur(sigma=0.2)\n",
    "images_aug = blurer.augment_images(images)\n",
    "print(\"images:\", images.shape)\n",
    "plt.imshow(images_aug[0].squeeze())\n",
    "print(\"images_aug[0].squeeze() \", images_aug[0].squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'Fliplr' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-150306c565ad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# always horizontally flip each input image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mimages_aug\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miaa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFliplr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# vertically flip each input image with 90% probability\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'Fliplr' object is not callable"
     ]
    }
   ],
   "source": [
    "from imgaug import augmenters as iaa\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)\n",
    "\n",
    "# always horizontally flip each input image\n",
    "images_aug = iaa.Fliplr(1.0)(images=images)\n",
    "\n",
    "# vertically flip each input image with 90% probability\n",
    "images_aug = iaa.Flipud(0.9)(images=images)\n",
    "\n",
    "# blur image 2 by a sigma of 3.0\n",
    "images_aug = iaa.GaussianBlur(3.0)(images=images)\n",
    "\n",
    "# move each input image by 8 to 16px to the left\n",
    "images_aug = iaa.Affine(translate_px={\"x\": (-8, -16)})(images=images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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